title: A note on dealing with missing standard errors in meta-analyses of continuous outcome measures in WinBUGS.
abstract: A meta-analysis of a continuous outcome measure may involve missing standard errors. This is not a problem depending on assumptions made about the population standard deviation. Multiple imputation can be used to impute missing values while allowing for uncertainty in the imputation. Markov chain Monte Carlo simulation is a multiple imputation technique for generating posterior predictive distributions for missing data. We present an example of imputing missing variances using WinBUGS. The example highlights the importance of checking model assumptions, whether for missing or observed data.
authors.affiliation: Centre for Bayesian Statistics in Health Economics, University of Sheffield, Sheffield, UK. J.W.Stevens@sheffield.ac.uk
authors.forename: John W
authors.initials: JW
authors.lastname: Stevens
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D015201:Meta-Analysis as Topic; D009010:Monte Carlo Method; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012984:Software